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AI in Oil and Gas Market - Growth, Trends, and Forecast (2020 - 2025)

AI in Oil and Gas Market - Growth, Trends, and Forecast (2020 - 2025)

  • March 2020
  • 122 pages
  • ID: 5881461
  • Format: PDF
  • Mordor Intelligence LLP
Up to $1125 off Until Jul 20th 2020

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The AI in Oil and Gas market was valued at USD 2 billion in 2019 and is expected to reach USD 3.81 billion by 2025, at a CAGR of 10.96% over the forecast period 2020 - 2025. As the cost of IoT sensors declines, more major oil and gas organizations are bound to start integrating these sensors into their upstream, midstream, and downstream operations along with AI-enabled predictive analytics.

- Oil and gas remains as one of the most highly valued commodities in the energy sector. In recent years, there has been an increased focus on improving efficiency, and reducing downtime has been a priority for the oil and gas companies as their profits slashed since 2014, due to fluctuating oil prices. However, as concerns over the environmental impact of energy production and consumption persist, oil and gas companies are actively seeking innovative approaches to achieve their business goals, while reducing environmental impact.
- In addition, the Oil and Gas Authority (OGA) is making use of AI in parallel ways, owing to the United Kingdom’s first oil and gas National Data Repository (NDR), launched in March 2019, using AI to interpret data, which, according to the OGA anticipations, is likely to assist to discover new oil and gas forecast and permit more production from existing infrastructures.
- The offshore oil and gas business use AI in data science to make the complex data used for oil and gas exploration and production more reachable, which lets companies to discover new exploration prospects or make more use out of existing infrastructures. For instance, in January 2019, BP invested in Houston-based technology start-up, Belmont Technology, to bolster the company’s AI capabilities, developing a cloud-based geoscience platform nicknamed “Sandy.”
- However, high capital investments for the integration of AI technologies, along with the lack of skilled AI professionals, could hinder the growth of the market. A recent poll validated that 56% of senior AI professionals considered that a lack of additional and qualified AI workers was the only biggest hurdle to be overcome, in terms of obtaining the necessary level of AI implementation across business operations.

Key Market Trends
Upstream Operations to Witness a Significant Growth

- Organizations across the world are trying to make the exploration and the production processes more efficient and optimized. The operations in this field are the major factors that are driving the usage of AI in oil and gas companies. The AI tools can help oil and gas companies in digitizing records and can automate the analysis of the gathered geological data and charts, which can lead to potential identification of issues, such as pipeline corrosion or increased equipment usage.
- Oil and gas companies can potentially gain crucial insights to improve their business outcomes in their upstream processes with the integration of AI software. This process would involve the feeding of curated data records and information from data sources to the software that could include structured documents, PDFs, handwritten notes, audio, or video files.
- The market is witnessing many investments by big players in the technology. For instance, in Jan 2019, BP invested in a technology start-up: Belmont Technology to strengthen the company’s AI capabilities by developing a cloud-based geoscience platform. The investment will be used to support BP’s ongoing work in exploring the application of cognitive computing and machine learning in its global oil and gas business.

North America is Expected to Hold a Significant Market Share

- Owing to the increasing adoption of AI technologies across the oilfield operators and service providers and the robust presence of prominent AI software and system suppliers, especially in the United States and Canada, the North American segment is anticipated to account for the largest share of the AI in the oil and gas market, over the forecast period.
- Factors, such as the strong economy, the high adoption rate of AI technologies across the oilfield operators and service providers, robust presence of prominent AI software and system suppliers, and combined investment by government and private organizations for the development and growth of R&D activities are poised to drive the demand for AI in oil and gas sector, in the region.
- ExxonMobil, one of the leading oil producers in the country, announced its plans to increase the production activity in the Permian Basin of West Texas, by producing more than 1 million barrels per day (BPD) of oil-equivalent by as early as 2024. This is equivalent to an increase of nearly 80 percent compared to the present production capacity.

Competitive Landscape
The AI in the oil and gas market is highly competitive and consists of several major players. In terms of market share, few of the major players currently dominate the market. The companies are continuously capitalizing on acquisitions, in order to broaden, complement, and enhance its product and service offerings, to add new customers and certified personnel, and to help expand sales channels.

- February 2020 - Royal Dutch Shell PLC has been expanding an online program that teaches its employees artificial intelligence skills, part of an effort to cut costs, improve business processes, and generate revenue. Artificial intelligence enables the company to process the vast quantity of data across the businesses to generate new insights, which can keep the ahead of the competition.
- October 2019 - Microsoft announced the collaboration with energy industry tech company Baker Hughes and AI developer C3.ai to bring enterprise AI technology to the energy industry via its Azure cloud computing platform. It would allow customers to streamline the adoption of AI designed to address issues like inventory, energy management, predictive maintenance and equipment reliability.

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